Building Profile Based Off Speech Patterns
Yes, there is. An extremely quick search found this:
Multimodal Speaker Identification Based on Text_and_Speech.

Let me tl;dr for you: (My abstract addition in Italics)

Novel method for speaker identification based on both speech utterances and their transcribed text. 

They first transcribed text of each speaker’s is processed by using probabilistic latent semantic indexing (PLSI) that models each speaker’s vocabulary which is closely related to his/her identity, function, or expertise. 

The speech to text used by users is DARPA's Efficient, Affordable, Reusable Speech-to-Text (EARS) Program in MetadataExtraction (MDE).

By using Melfrequency cepstral coefficients (MFCCs) and dynamic range is quantized to a number of predefined bins in order to compute MFCC local histograms for each speech utterance, which is time-aligned with the transcribed text. 

To test they used RT-03 MDE Training Data Text and Annotations corpus distributed by the Linguistic Data Consortium.

As for results: Identification rate versus Probe ID when 44 speakers are employed. Average identification rates for (a) PLSI: 69%; (b) MFCCs: 66%; (c) Both: 67%.

If you need more papers related, you could use a tool like https://the.iris.ai/ to find related papers.

Post edit: Hopefully now this post complies with the standards.